System for handling frequently asked questions in a natural language dialog service

ABSTRACT

A voice-enabled help desk service is disclosed. The service comprises an automatic speech recognition module for recognizing speech from a user, a spoken language understanding module for understanding the output from the automatic speech recognition module, a dialog management module for generating a response to speech from the user, a natural voices text-to-speech synthesis module for synthesizing speech to generate the response to the user, and a frequently asked questions module. The frequently asked questions module handles frequently asked questions from the user by changing voices and providing predetermined prompts to answer the frequently asked question.

PRIORITY DOCUMENTS

The present application is a continuation of U.S. patent applicationSer. No. 11/675,166, filed Feb. 15, 2007, which is a continuation ofU.S. patent application Ser. No. 10/326,692, filed Dec. 19, 2002, whichclaims priority to provisional application No. 60/374,961, filed Apr.23, 2002, the contents of which are incorporated herein by reference.

RELATED APPLICATIONS

This case is related to commonly assigned U.S. patent application Ser.No. 10/235,266, filed on Dec. 19, 2002, the contents of which areincorporated herein by reference (attorney docket number 2002-0093A).This case is related to commonly assigned U.S. patent application Ser.No. 10/235,295, filed Dec. 19, 2002, the contents of which areincorporated herein by reference (attorney docket number 2002-0093).This case is related to commonly assigned U.S. patent application Ser.No. 10/326,691, filed Dec. 19, 2002, the contents of which areincorporated herein by reference (attorney docket number 2002-0051). Thepresent application is related to commonly assigned U.S. patentapplication Ser. No. 10/160,461 filed May 31, 2002, Attorney DocketNumber 2001-0320, the contents of which are incorporated herein byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to dialog systems and more specifically toan extended spoken language understanding module for handling frequentlyasked questions.

2. Discussion of Related Art

Voice-enabled applications are becoming more widespread as automaticspeech recognition (ASR), spoken language understanding (SLU), dialogmanagement (DM) and text-to-speech (TTS) synthesizers improve. Thesevoice-enabled applications represent an evolution of traditional helpdesks that are currently available on the web or supported by humanagents. The goals of a voice-enabled help desk include call routing toappropriate agents or departments, providing a wealth of informationabout various products and services, and conducting problem solving ortrouble shooting.

Speech and language processing technologies have the potential ofautomating a variety of customer care services in large industry sectorssuch as telecommunications, insurance, finance, travel, etc. In aneffort to reduce the cost structure of customer care services, many ofthese industries are depending more heavily on complex Interactive VoiceResponse (IVR) menus for either automating an entire transaction or forrouting callers to an appropriate agent or department. Several studieshave shown that the “unnatural” and poor user interfaces of such menustend to confuse and frustrate callers, preventing the callers fromaccessing information, let alone obtaining, in many cases, obtaining thedesired service they expect. For example, studies show that over 53% ofsurveyed consumers say that automated IVR systems are the mostfrustrating part of customer service. In one survey, 46% of consumersdropped their credit card provider and 30% of them dropped their phonecompany provider due to poor customer care.

The advent of speech and language technologies have the potential forimproving customer care not only by cutting the huge cost of runningcall centers in general but also by providing a more naturalcommunication mode for conversing with users without requiring them tonavigate through a laborious touch-tone menu. This has the effect ofimproving customer satisfaction and increasing customer retention rate.These values, which collectively form the foundation for an excellentcustomer care experience, have been evident in the AT&T Call Routing“How May I Help You” service that provides national consumer servicesvia an automated spoken dialog system.

Soon, speech and language technologies will play a more pivotal role incustomer care service and in help desk applications where the objectivesinclude call routing and accessing information, as well as solvingtechnical problems, sales, recommendations, and trouble shooting. Manycomputing and telecommunication companies today provide some form of ahelp desk service through either the World Wide Web or using a humanagent. There is an opportunity for spoken natural language interfaces toplay a much bigger role in this industry.

FIG. 1 illustrates the basic components required for human-computerinteractive spoken dialog systems 10. The customer 12 speaks andprovides an audible voice request. An automatic speech recognition (ASR)module 14 recognizes the speech and provides the text of the speech to aspoken language understanding (SLU) module 16 that parses the naturallanguage input into relevant information to determine the substance ofthe customer inquiry. A dialog manager (DM) 18 receives the informationregarding what the customer asked and generates the substance of theresponse, which is transmitted to a language generator 20 for generatingthe text of the response. The response text is transmitted to atext-to-speech (TTS) module 22 for generating a synthetic voice that“speaks” the response to the customer 12.

Further, some systems that are deployed are programmed to follow aparticular dialog flow to lead the customer to the proper destination orinformation. Often, various consumers will have common questions thatare asked that perhaps may be outside the designed dialog flow. Previoussystems fail to adequately and efficiently handle these kinds offrequently asked questions.

Current technologies fail to enable companies to afford generatingautomated help desks. Handcrafted systems require manual training,segmenting and labeling of data in preparation for the voice userinterface in the particular domain of the company. The data required forhandcrafted systems may comprise hours and hours of scripted dialog withhumans and the computer. The scripted computer-human interactions arestudied and processed in a labor-intensive manner to train the newspoken dialog service. Such systems are time-consuming and costly tobuild, thus effectively preventing many companies from participating andreceiving the improved customer care service that can be provided.

SUMMARY OF THE INVENTION

What is needed in the art is a help desk service that provides a morenatural information exchange between a user and the help desk thatincreases customer satisfaction. An advantage of the present inventionis to enable a natural languages help desk application that providesincreased ease of use for customers calling the help desk.

Another advantage of the present invention is to improve customerrelationships with companies using a natural language help desk thatunderstands and interacts with users in a more efficient and pleasantmanner, especially when handling frequently asked questions. The helpdesk according to the present invention includes an extended spokenlanguage understanding module that includes further features of handlingfrequently asked questions.

Embodiments of the present invention include systems, methods andcomputer-readable medium-stored instructions for providing avoice-enabled interface. In an exemplary embodiment, a voice-enabledhelp desk service comprises (1) an automatic speech recognition modulehaving a general-purpose acoustic model and a domain-specific model,wherein the general-purpose model is used for bootstrapping at aninitial deployment of the voice-enabled help desk service and thedomain-specific model is used to adapt the automatic speech recognitionmodule after deployment; (2) a spoken language understanding module thatperforms text normalization, entity extraction and semanticclassification using a boosting approach that balances human-craftedrules with available data, the spoken language understanding moduleincluding a frequently asked questions module; (3) a dialog managementmodule that comprises an interpreter, finite state machine engine and anaction template; and (4) a natural voices text-to-speech synthesismodule for synthesizing speech. The frequently asked questions modulehandles frequently asked questions from the user by changing voices andproviding prompts and answers calculated to move the user from thequestion back to the main dialog.

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. Thefeatures and advantages of the invention may be realized and obtained bymeans of the instruments and combinations particularly pointed out inthe appended claims. These and other features of the present inventionwill become more fully apparent from the following description andappended claims, or may be learned by the practice of the invention asset forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing advantages of the present invention will be apparent fromthe following detailed description of several embodiments of theinvention with reference to the corresponding accompanying drawings, inwhich:

FIG. 1 illustrates the components of a general spoken dialog system;

FIG. 2 illustrates the general components used according to the presentinvention;

FIG. 3 illustrates an extended SLU module according to an aspect of thepresent invention;

FIG. 4 illustrates an exemplary dialog manager architecture for use inthe present invention;

FIG. 5 illustrates a high-level user interface according to an aspect ofthe invention; and

FIG. 6 illustrates word accuracy results.

DETAILED DESCRIPTION OF THE INVENTION

The present invention may be understood according to exemplaryembodiments disclosed herein. When creating a spoken dialog service,experimental results are presented in terms of recognition accuracy,understanding accuracy and call completion rate.

There are several technology requirements needed for a voice-enabledhelp desk application. FIG. 2 illustrates some of the core necessarycomponents of such a system 30, including an automatic speech recognizer(ASR) 32 capable of recognizing large-vocabulary spontaneous speech, anextended language understanding module (SLU) 34 that parses the naturallanguage input into relevant information, a dialog manager (DM) 36 thatoperates in a mixed-initiative mode, a language generation module 38 anda text-to-speech module (TTS) 40 capable of generating high-qualitysynthesized voices fonts. For example, AT&T Labs' Natural Voices speechtechnologies include customized natural voice TTS engines that may beused for a variety of applications.

The present disclosure provides improvements in various modules shown inFIG. 2 that improve the cost, deployment time, customer relationshipcapability, and overall user experience for help desk applications. Thefollowing description provides further information used for buildinghelp desk applications quickly and efficiently.

Those of skill in the art will appreciate that other embodiments of theinvention may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Embodiments may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination thereof) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.Accordingly, as used herein, the term “the system” will refer to anycomputer device or devices that are programmed to function and processthe steps of the method.

When initially building a help desk application, an aspect of theprocess that takes the longest amount of time relates to transcriptionand annotation of domain-specific speech used in building the underlyingrecognition and understanding models. “Domain-specific speech” relatesto the speech that is within the expected subject matter of aconversation with a person. For example, a travel-related web-site willhave “domain-specific” speech such as “destination,” “one-way,”“round-trip” etc. When the help desk is deployed for a specific purposeor “domain,” the recognition and understanding modules can maintain ahigh level of accuracy.

When a help desk application is being developed for a company,information about that company must be gathered in order to train therecognition and understanding modules. The process for collecting andannotating speech data is not only expensive and laborious; it delaysthe deployment cycle of new services. The process according to thepresent invention of building help desk services begins by “mining” and“reusing” data and models. Data mining is done not only from othersimilar application domains such as telecommunications, insurance,airline, etc, but also from relevant emails, web pages and human/agentrecordings. See U.S. patent application Ser. No. 10/326,691 (AttorneyDocket 2002-0051) for more details regarding this process.

As part of the labeling process, sentences are annotated for speechunderstanding purposes. The sentences, as mentioned above, can come fromany source such as emails or web-site data. This is done in two phases.The first phase includes identifying and marking domain-specific anddomain-independent value entities such as phone numbers, credit cardnumbers, dates, times, service offerings, etc. The second phase includesassociating each input sentence with one or more semantic tags (orclasses) that identify the “meaning” of a user's request. These tags canbe both general and application-specific and are structured in ahierarchical manner. For example, phrases such as “may I hear thisagain” and “yes what products do you offer” can be tagged as “discourserepea” and “discourse_yes, info_products” respectively.

Regarding the automatic speech recognition module 32, accuraterecognition of spoken natural-language input for help desk applicationsrequires two components: (1) a general-purpose subword-based acousticmodel (or a set of specialized acoustic models combined together), and(2) a domain-specific stochastic language model (or a set of specializedlanguage models). Creating help desk applications imposes two challengesin building these models. The present invention enables the ability tobootstrap the service during an initial deployment.

Once all the available data is obtained, and the phases of (1)identifying and marking domain-specific and domain-independent valueentities and (2) associating each input sentence with one or moresemantic tags (or classes) that identify the “meaning” of a user'srequest are complete, the system can be initially deployed. While therecognition accuracy is not always at an acceptable level at this stage,often enough data can be gathered from company emails and web-site datato at least deploy the help desk application.

The invention provides for adapting the help desk service astask-specific data becomes available through live use of the service. Inthe case of acoustic modeling, according to the present invention, thehelp desk ASR 32 engine initially uses a general-purposecontext-dependent hidden Markov model. This model is then adapted usinga Maximum a posteriori adaptation once the system is deployed in thefield. See, e.g., Huang, Acero and Hon, Spoken Language Processing,Prentice Hall PTR (2001), pages 445-447 for more information regardingMaximum a posteriori adaptation.

When generating the ASR module 32, stochastic language models arepreferred for providing the highest possibility of recognizing wordsequences “said” by the user 42. The design of a stochastic languagemodel is highly sensitive to the nature of the input language and thenumber of dialog contexts or prompts. A stochastic language module takesa probabilistic viewpoint of language modeling. See, e.g., Id., pages554-560 for more information on stochastic language models. One of themajor advantages of using stochastic language models is that they aretrained from a sample distribution that mirrors the language patternsand usage in a domain-specific language. A disadvantage of such languagemodels is the need for a large corpus of data when bootstrapping.

Task-specific language models tend to have biased statistics on contentwords or phrases and language style will vary according to the type ofhuman-machine interaction (i.e., system-initiated vs. mixed initiative).While there are no universal statistics to search for, the inventionseeks to converge to the task-dependent statistics. This is accomplishedby using different sources of data to achieve fast bootstrapping oflanguage models including language corpus drawn from, for example,domain-specific web site, language corpus drawn from emails(task-specific), and language corpus drawn a spoken dialog corpus(non-task-specific).

The first two sources of data (web sites and emails) can give a roughestimate of the topics related to the task. However the nature of theweb and email data do not account for the spontaneous-speech speakingstyle. On the other hand, the third source of data can be a largecollection of spoken dialog transcriptions from other dialogapplications. In this case, although the corpus topics may not berelevant, the speaking style may be closer to the target help deskapplications. The statistics of these different sources of data arecombined via a mixture model paradigm to form an n-gram language model.See, e.g., Id., pages 558-560. These models are adapted oncetask-specific data becomes available.

Regarding the text-to-speech synthesis module 40, the extensive callflow in help desk applications to support information access and problemsolving, and the need to rapidly create and maintain these applications,make it both difficult and costly to use live voice recordings forprompt generation. The TTS module 40 plays a critical role in the newbreed of natural language services where up-to-the-minute information(e.g., time and weather) and customization to an individual's voice arenecessary. According to the present invention, the TTS module 40provides a large variety of distinctive voices and, within each voice,several speaking-styles of many different languages. This is helpful for“branding” of help desk services.

The TTS engine 40 uses AT&T Labs Natural Voices technology and voicefonts. See, e.g., M. Beutnagel and A. Conkie and J. Schroeter and Y.Stylanianou and A. Syrdal, “The AT&T Next Generation TTS System”, JointMeeting of ASA, EAA and DAGA, 1999. Due to automation of the voicecreation process, new and customized voice fonts can be created in lessthan a month. Including task-specific data (i.e., materials relevant tothe application) can assure a higher quality TTS voice.

For example, the main voice font used in an exemplary help desk TTSengine 40, named “Crystal”, is trained with over 12 hours of interactivedialogs between human agents and customers. In the help desk applicationdescribed below, over 8 different voice fonts have been used within thesame application for presenting different languages and dialog contexts.Any number of different voice fonts may be provided in a help deskapplication.

Regarding the spoken language understanding (SLU) module 34, textnormalization is an important step for minimizing “noise” variationsamong words and utterances. This has the potential of increasing theeffective size of the training-set and improving the SLU 34 accuracy.The text normalization component is essentially based on usingmorphology, synonyms and other forms of syntactic normalization. Themain steps include stemming, removal of disfluencies, non-alphanumericand non-white space characters and using a synonyms dictionary.

An important functionality of an SLU module 34 is the ability to parsethe input speech into meaningful phrases. Parsing for help deskapplications is simplified to a process of identifying task-specific andtask-independent entities (such as phone numbers, credit card number,product type, etc.). Each entity module is built using standardcontext-free grammar that can be represented by a finite statetransducer. Following text normalization, the system identifies entitiesby composing each input text string with all active entity modules. Forexample, the sentence “my bill for January 2^(nd)” is parsed as “my billfor <Date> January 2^(nd)</Date>”. Entity extraction not only helps toprovide the DM 36 with the necessary information to generate a desiredaction but also it provides some form of text normalization forimproving the classification accuracy.

FIG. 3 illustrates an extended SLU module 34 for use in applicationssuch as a help desk. The extended SLU 34 enables a more efficient meansfor responding to frequently asked questions. Customer care servicesthat use natural language understanding technology to perform callrouting are typically limited to 10-100 call types (categories). The lownumber of categories and the lack of deeper understanding of the inputlimit these systems from providing sufficiently detailed informationabout the task. Typically, it is therefore not possible to answer users'specific questions directly. Instead, most systems give very generalanswers, and users are left to deduce the answer to their specificquestions.

In an aspect of the present invention, a method extends the capabilitiesof natural language dialog systems to directly answer users' specificquestions without increasing the number of call types. Referring to FIG.3, in a spoken dialog system, user utterances are transformed into text“Q” via an ASR 32. The SLU module 34 converts this text into a semanticrepresentation, based on which a DM module 36 decides the next action totake or next prompt to play to the user. A classifier-based SLU 34classifies the input using a classification module 43 into one or manypre-determined call types “C” that form the basis of the DM's 36 nextaction.

The extended SLU 34 processes the output from the classification module43 by selecting data from a question and answer (QA) table 46 for thecall type “C”. In one aspect of the invention, a plurality of QA tablesis stored and available from which to select a particular QA tableaccording to the call type “C.” Once the QA table is selected, thesystem searches 44 the QA table for a question similar to the currentutterance or text “Q”. The QA table stores a series of correspondinganswers to various questions. The SLU 34 selects the most similarquestion to the text “Q” from the QA table and outputs the correspondinganswer 45. If a question with a high enough similarity is found in theQA table 46, the system selects the most similar question to “Q” from alist of questions in the table. The text “Q”, call type “C” and answer“A” are transmitted to the DM 36.

An example of the process of performing the FAQ similarity computationfollows. In an initialization process, each question FAQ_(j) in the QAtable is represented as a vector: (faq_(j1), faq_(j2), faq_(j3), - - -faq_(jm)) assuming there are “m” unique words in all the questions inthe QA table. The term faq_(ji) equals tf_(ji) times idf_(i), wheretf_(ji) is the term frequency of word_(i) (1 if word_(i) is present, 0if it is absent in FAQ_(j)). The term idf_(i) is defined as an inversedocument frequency of word_(i) and equals log N/(n_(i)). The term N isthe total number of questions in QA table and n_(i) is the number ofquestions containing the word_(i).

${{Sim}( {Q,{FAQ}_{j}} )} = \frac{\sum\limits_{i}^{\;}\; {q_{i} \cdot {faq}_{ji}}}{\sqrt{{\sum\limits_{i}^{\;}\; ( q_{i} )^{2}} + {c(0.5)}^{2}} \cdot \sqrt{\sum\limits_{i}\; ( {faq}_{ij} )^{2}}}$

For each incoming question Q to be answered, a system operatingaccording to the present invention performs the following steps: (1)calculating a vector representation of the question Q (q₁, q₂, q₃- - -q_(m)), wherein q_(i) equals tf_(i) times idf_(i) and tf_(i) is 1 ifword_(i) is present and 0 if it is absent in the question Q. The termidf_(i) is the inverse document frequency of the word_(i), calculatedabove during the initialization process; (2) finding the number of words“c” in the incoming question that are not found in the “m” unique wordsseen in the all FAQs in the QA table; (3) for each question FAQ_(j) inthe QA table, calculating a similarity computation:

(4) selecting the FAQ_(j) that has maximum value of Sim(Q, FAQ_(j)); and(5) reading the corresponding answer from QA table. In this manner, asystem operating according to the present invention will perform thesimilarity computation according to the above description ofinitialization and for each incoming question calculating and processinga similarity value for use in selecting the appropriate answer from theQA table. This particular similarity computation is an example of one ofmany similarity computational methods that may be used. Accordingly, theinventors recognize that unless specifically claimed, the particularsimilarity computation is not limited to the above formula.

Using the above-mentioned principles, a dialogue and classification ofquestions can take place. For example, an utterance like “Can yoursoftware run on Linux” is classified as a call type“tech_compatibility”. One could argue that it should be classified asLinux_compatibility, but such a classification would requireWindows_compatibility, Unix_compatibility, etc. Classification base SLUsare ill suited for such an explosion in call types.

As another example of the process, suppose the phrase “Can I hear afemale custom voice demonstration . . . hum . . . in Spanish?” is inputto the SLU 34. The SLU 34 output may look like:

- <!-- Spanish Custom Female and default Demo (Male not available) --> -<spanishCustomFemale mode=“queued”> <prompt id=“intro” type=“TTS”src=“HelpDesk:Prompts:demo_1004_pc_v4”file=“../prompts/demo_1004_pc_v4.ul”>OK, I can show you our female voicein Spanish. This voice will be available with Release 1.1 on Decemberfirst.</prompt> <prompt id=“voiceFemale” type=“TTS”src=“HelpDesk:Prompts:demo_1014_pc_v1”file=“../prompts/demo_1014_pc_v1.ul”>Here it is.</prompt> <audiosrc=“system:sounds:sil500ms” /> <audiosrc=“HelpDesk:DemoVoices:spanish_fl” /> <prompt id=“end” type=“TTS”src=“HelpDesk:Prompts:demo_1006_pc_v2”file=“../prompts/demo_1006_pc_v2.ul”>If you're interested in talking tosomeone about our Spanish voice, just ask for sales.</prompt> <varname=“rejection” value=“0” /> <var name=“maxRejection” value=“1” /></spanishCustomFemale>The above XML output only serves as an example of one way the inventionmay be implemented. This particular implementation is not meant to belimiting of the scope of the invention.

In previous systems, the dialog manager responds to the question “Canyour software run on Linux?” by playing a prompt that informs the userof all the platforms the software is compatible with, even though theuser wanted to know only about Linux. An aspect of the present inventionenables the system to generate a more appropriate answer to thequestion, such as: “Yes, our software can run on Linux”.

As shown in FIG. 3, the SLU 34 is extended to allow such responses bybeing provided with the QA table 46, possibly one for each call type.These can be viewed as frequently asked questions and their answers,possibly partitioned by call types.

To measure the similarity between user questions and questions in the QATable 46, the system uses cosine similarity within the vector spacemodel well known in the information retrieval field. According to thepresent invention, the system normalizes the vectors with the querylength. As mentioned above, classifier-based SLUs and informationretrieval technology are well known in the state of the art. This aspectof the invention combines classifier-based SLUs and informationretrieval technology to answer direct questions.

The innovations disclosed herein allow development of effective spokendialog-based help desks. They allow improvements in the questionanswering capability, simply by adding new questions and answers in theQA tables 46. Such capabilities can reduce the cost of customer supportfor companies and provide a mechanism for inserting immediateinformation into the system without involving any alteration to the ASR,SLU or the DM modules. Further information regarding the QA table 46 andanswering frequently asked questions will be provided below withreference to FIG. 6.

An aspect of the present invention relates to semantic classification ofutterances. The system categorizes each utterance into one or moresemantic classes. A machine learning approach is taken for this task.The classifier 43 is trained using a corpus of collected utterances thathave been annotated using a predefined set of semantic tags.

To train an exemplary classifier 43 according to the present invention,the system uses a technique called boosting. The basic idea of boostingis to combine many simple and moderately inaccurate prediction rulesinto a single rule that is highly accurate. Each of the base rules istrained on weighted versions of the original training set in which the“hardest” examples—i.e., those that are most often misclassified by thepreceding rules—are given the greatest weight. The base rules are thencombined into a single rule by taking a kind of majority vote. The firstpractical and still most widely studied boosting algorithm is Freund andSchapire's AdaBoost algorithm. See, e.g., Y. Friend and R. E. Schapire,“A decision-theoretic generalization of on-line learning and anapplication to boosting”, Journal of Computer and Systems Sciences,1997, for an overview of work on boosting.

In a preferred embodiment of the invention, the system uses animplementation of boosting developed by Schapire and Singer calledBoosTexter. See, e.g., U.S. Patent Application No. 60/306,283,incorporated by reference above. In this implementation, each rule makesits predictions based simply on the presence or absence of a word orshort phrase in the utterance. Like most machine-learning methods,boosting is heavily data driven, and so requires a good number ofexamples.

In developing help desk applications, it is often necessary to deploythe system before a sufficient number of examples have been collected.To get around this difficulty, the present invention uses humanknowledge to compensate for the lack of data. In particular, the systemuses a modification of boosting developed by Schapire et. al. thatadmits the direct incorporation of prior knowledge so that a classifier43 is built by balancing human-crafted rules against what little datamay be available. See the patent applications incorporated above formore information on using prior knowledge to boost the development ofthe classifier 43.

The human-built rules have a simple form and need not be perfectlyaccurate; for instance, one rule may state that if the word “demo”occurs in the utterance, then the user probably wants to hear ademonstration of some sort. Incorporating prior knowledge in aprobabilistic fashion allows rapid deployment and a more effective wayto add new tags throughout service evolution.

Regarding the DM module 36, significant challenges exist on how to buildand easily maintain large-scale voice-enabled applications. The DMmodule 36 is designed according to the present invention to address thechallenges of building voice-enabled applications. FIG. 4 illustrates anexample of the architecture for the DM 36 of the present invention. TheDM 36 is a particularly important issue for help desk applications wherethe nature of the information can be constantly changing. The complexityof the dialog modeling and the lack of adequate authoring tools cancompromise the value and effectiveness of an automated help deskservice.

The approach proposes, through general dialog patterns, a unified viewto represent a human-machine dialog flow structure of commonly acceptedreference models for mixed-initiative systems. A general engine operateson the semantic representation provided by the extended SLU 34 andcurrent dialog context 51 (dialog strategy) to control the interactionflow. To describe the human-machine interaction, the system extends thetraditional approach of finite state machines (FSM). FSMs are attractivemechanisms for dialog specification since they are (a) a directtranslation of call flow specifications, (b) easy to augment withspecific mixed-initiative interactions (c) practical to manage extensivedialog context. However, the use of FSM as mechanisms for managing thedialog flow is discussed, other means are also contemplated for thisprocess. For example, dialog motivators may also be used as well asother mechanisms that are developed.

FIG. 4 illustrates the FSM engine 52 and FSM 64 in the DM 36 accordingto the present invention. The SLU module 34 processes the ASR outputinto, for example, the semantic representations in XML. The DM 36 shownhere includes modules such as an interpreter 50, FSM engine 52, andaction set templates 54. The FSM engine 52 controls the actions taken inresponse to the interpreter 50 output. Within the FSM structure, eachinformation state provides support for general user interface patternssuch as correction, start-over, repeat, confirmation, clarification,contextual help, and context shifts. Topic tracking is a feature thatprovides the infrastructure for rendering information. Generalconversation topics are managed by a subdialog that (a) handles, in adeclarative way, new topics, (b) specifies the level of details pertopic, and (c) allows context shift to take place at any point in thedialog.

According to the present invention, a dialog D is a tuple presented bythe following expression D=<q_(o),Q,Θ,δ> where q_(o)εQ is the initialstate, Q is a finite set of nodes, F⊂Q is a set of final nodes, Θ is thesemantic representation produced by the interpreter 50, δ: Q×ΘΘQ is atransformation function q′=δ(q, i), where q, q′ are respectively thecurrent and the next state and i is the current concept. The historymechanism can be modeled with the following tuple H=<T, C, φ> where T isthe set of the current and past dialog turns including states variablesand produced actions, c is the set of checkpoints or rollback states andφ: T×C→{true,false} is a Boolean function retuning true orfalsedepending on the rollback strategy. Finally, the interpreter 50 maps toa tuple Θ=<

,H,Σ,σ>, where

is a set of logical predicates, H is the dialog history, Σ is the set ofinput concepts and system events (e.g., semantic frames or slots), σ:

×H→Σ is a transformation function i=σ( r, h) where r is the set of rulesthat applies to the input frame and h is the current dialog history.

The interpreter 50 is responsible for providing a semanticinterpretation of the concept categorization and the named entitiesprovided by the SLU module 34. Logical predicates described in the rulesfile 62 allow the interpreter 50 to rank classes and assign a contextualinterpretation to the input. An example of the output from theinterpreter 50 follows:

if (slu.info_demo) { demo = getDemo(“female”,“custom”,“spanish”) n =history(demo) if (n>=0 && n< maxDemo) // demo already presented returndemo[n] else return noMoreDemo }

The interpreter 50 also has access to the state variables 66, the dialoghistory 68 and user profiles 70. The user profile communicates with auser profile agent 56 that uses information 57 associated with thelocation of the user, the weather or local time for the user, or a blacklist of users or web content. The history mechanism model allows the DM36 to capture situations where the request is under-specified or toogeneral. For example, if the current topic has a missed mandatoryattribute, the dialog engages a clarification sub-dialog in order toprovide the missed information. Moreover, the history provides supportfor correction (rollback one dialog turn) or repeat requests. This lastfeature includes both explicit and semantic repeat requests. Situationswhere the user utters “please repeat that” or “what was the cost?” arethen handled correctly by repeating the previous repeatable information.

An action template 54 receives actions as a result of the operation ofthe FSM engine 52. The action template 54 represents a template-basedoutput generator. An XML-markup language describes the dialog actions(e.g., prompting, grammar activation, database queries, and variablevalues updates) and the topic structures. New topics and subtopics canbe added, removed or updated at this level without changing the basicservice logic. At run-time, the system translates the output by a XSLstyle sheet 72 either to Voice XML 60 (voice content through means sucha telephone service) or to HTML 58 (text content) for output authoring.In this way, the presentation layer and the dialog structure for thetopic sub dialog are completely separated from the service logic and areeasy to maintain with traditional authoring tools.

An important aspect of the present invention relates to the Userinterface (“UI”). The UI is what the customer experiences wheninteracting with a system and plays an important role in providingquality service and user experience. There are two aspects in UI designfor help desk applications: (a) usability goal—increasing the likelihoodof call completion with minimal user confusion by supporting contextshift in the dialog, providing information and help whenever necessary,and by learning how users interact with the system and propagating thatknowledge into improving the various technology components; and (b)personality—creating an agent personality from synthesized speech thatoften lacks emotions. The system, according to an aspect of the presentinvention, uses a screenwriting dialog technique where a back story iscreated for the agents based on a set of desired personality traits(e.g., cheerful, trustworthy, calm, strong-willed, helpful, etc). As anexample, a one-page description of the agent life history may bedescribed and prompts are written “in-character”. A back story for anagent may be reflected in the agent making statements such as “I have toadmit I'm having problems understanding you,” “Let's stop for just aquick second,” or “I honestly can't image using anything but the best!”

A plurality of different synthesized voices can be used conveyingdifferent information to the user. For example, an application mayemploy eight different voices for communicating different kinds ofinformation to the user. The dialog strategy begins with the phrase “HowMay I Help You?” The system supports natural language input and contextshift throughout the application. In the service application describedbelow, several different opening prompts are deployed which help toanalyze user feedback and how that is affected throughout the course ofthe dialog. For example, the top-level prompts can be of the category ofdemonstration and command-like hints: “Hi you are listening to AT&T LabsNatural Voices text to speech. I can show you a demo or answer yourquestions on topics like general information, pricing, or new languages.Don't hesitate to interrupt me at any time. Now, how can I help you?”Another category of top-level prompts is to promote a request for a demoonly: “Hi you are listening to AT&T Labs Natural Voices text to speech.I can show you a demo or answer your questions. Now, how can I helpyou?” Or, the prompt can be designed to elicit questions regarding ademo or some hints on how to ask questions: “Hi you are listening toAT&T Labs Natural Voices text to speech. I can show you a demo or answeryour questions. For example, you can ask me about the products we sellor how much they cost. Now, how can I help you?”

The various categories of top-level prompts and the examples above havebeen shown to mold and affect the user's requests. For example, studiesshow that the word content in user utterances varies for each categoryof top-level prompt. Accordingly, the particular phrases used in thedialog are chosen to prompt the user to use certain phrases or termsthat are more likely to be understood by the system. FIG. 5 illustratesan exemplary high-level UI for a help desk application. In this case,the dialog begins with “How may I help you?” 110. The user may provideseveral responses. Suppose that the user says “I want to hear German.” A<play demo> routine 112 then runs to provide an example of German.Following this step, the system sends the user 42 to the prompt“anything else?” 114. If the user says “how much does it cost?”, thenthe system plays an <info prompt> and asks “Would you like to hearmore?” 116. A “yes” returns the state to the <info_prompt> and questionof wanting to know more and a “no” forwards the user to the prompt“anything else?” 114. From the prompt 114, a context shift 118 may occurwhere the user may ask for information or to hear another demonstration.Similarly, a context shift may occur between the prompt 116 and thestate designated to play the demonstration 112.

An exemplary help desk application relates to a service for the AT&TLabs Natural Voices—a business that specializes in selling and marketingTTS products and voice fonts. The so-called TTS help desk took less thanthree months to design, develop and test. At one point followingdeployment, the TTS help desk received over 1000 calls per month frombusiness customers. The service performs call routing to specializedagents (such as sales, technical, customer service) and providesinformation about the various products and services. The system alsoprovides callers with a variety of demonstrations of the different voicefonts and languages.

An aspect of the present invention is the process of creating such helpdesk application. The initial data collection effort in building thehelp desk is primarily based on a large set of email interactions thattook place prior to launching the business. Utterances stored fromconversations associated with the products and services are manuallyextracted and annotated using a set of 62 broad semantic tags thatdescribe the types and characteristics of the products and services thebusiness was able to support. These tags are categorized into broadergroupings such as agent, general information, help, technical and website. Presented below are some benchmark results for the TTS help desk:(a) ASR, (b) question/answering, and (c) task completion rates, on a setof 1000 dialogs.

After the deployment of the TTS help desk, ASR results can be analyzed.Detailed analysis of the corpus shows that it exhibits a mixed sample oftwo language styles: key phrases and spontaneous language. The averagenumber of user turns is 3.3 with 27% of users engaging longerinteractions than average. Although there are roughly 75 possibleprompts on each dialog turn, in studies the prompt contexts have beenclustered into four types: generic, confirmation, language and help.Each context corresponded to a stochastic language model and wasbootstrapped in three different ways: using web data, email data or aninventory of a human-machine (HD) database acquired from other dialogapplications.

FIG. 6 shows 600 overall word accuracy of the TTS help desk system onthe 1000 dialog interactions. These results show that the help desk wasable to achieve 59% word accuracy without any formal data collection.When sufficient data was available (after 6 months from systemdeployment), the accuracy jumped to nearly 68%.

Help desk applications that are available on the web often provide anextensive list of Frequently Asked Questions (FAQs) to help users accessdetailed information in a straightforward manner. In text understanding,there are many systems that exist today that perform question/answering.For example, the AT&T 10-NAUT system (see www.ionaut.com) can provideanswers to queries requesting entity information such as names anddates.

According to the present invention, the system incorporates aquestion/answering module to help users with task-specific FAQs. The FAQmodule may be incorporated in the SLU 34 as shown in FIG. 4 or may beassociated with another element of a spoken dialog system. In apreferred embodiment, the QA module is provided in the form of a QAtable 46 as shown in FIG. 4, wherein the questions and answers areextracted from previous callers to the system. The accuracy of thismodule is improved by partitioning the table into smaller subsets, eachcorresponding to a semantic tag. During a test call, if a user asks aquestion which matches closely to one found in the QA table, the answeris automatically passed to the DM 36 along with any semantic tags (fromthe classifier) and entities. String matching is performed using cosinesimilarity within the vector space model well known in the informationretrieval field. Better matching accuracy was observed if normalizationof the vectors is carried out with the query length as opposed to theentire data set.

Regarding the question and answer results among some data collected bythe inventors of the present invention, a small set of 250 questionsfrom one specific tag were identified as potential FAQs and grouped into81 distinct sets. Thus, for each answer there were potentially one ormore questions. Given a question with a specific semantic tag, the taskwas to identify whether the system can identify the appropriate answer.The 81 sets of questions constituted as the training set were indexedusing a vector space model. The test set consisted of 336 questions ofwhich only 69 corresponded to valid questions, and the remaining areadded to evaluate the robustness of the technique. At a given operatingpoint, precision and recall were computed at 0.9 and 0.94, respectively,thus providing evidence of the effectiveness of the question-answeringaspects of the present invention.

The benefits of the help desk system and method disclosed above havebeen shown since a bootstrapped system, after three months ofdeployment, illustrated an average 85% task completion rate. Althoughthe functionalities of the system were continuously changing during thatperiod of time, the successful statistic shows consistent improvement inthe system. Results show that (a) the ASR accuracy which was initiallyat 59% through bootstrapping was improved to 68% following 6 months ofsystem deployment; (b) question/answering results were at 0.9 and 0.94for precision and recall, respectively; and (c) the latest release ofthe system performs at 84% and 85% semantic classification rate and taskcompletion rate, respectively. These statistics only provide someinformation regarding the success of the approaches described above andare not meant to be limiting in any manner given that further researchand refinement of the inventive concepts will improve the accuracy ofhelp desk applications.

Embodiments within the scope of the present invention may also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions or data structures. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or combination thereof) to a computer, the computerproperly views the connection as a computer-readable medium. Thus, anysuch connection is properly termed a computer-readable medium.Combinations of the above should also be included within the scope ofthe computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,objects, components, and data structures, etc. that perform particulartasks or implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Although the above description may contain specific details, they shouldnot be construed as limiting the claims in any way. Other configurationsof the described embodiments of the invention are part of the scope ofthis invention. Accordingly, the appended claims and their legalequivalents should only define the invention, rather than any specificexamples given.

1. A method of responding to user utterances in a spoken dialog service,the method comprising: receiving a user utterance; identifying aquestion and an answer selected from a question and answer table basedon a similarity computation that calculates a similarity between theuser utterance and at least one question in the question and answertable and a set of words in the user utterance that are not found in aset of unique words in the question and answer table; and presenting ananswer to the user utterance from the identified question and answer. 2.The method of claim 1, further comprising: classifying the recognizedtext associated with the user utterance; and selecting the question andanswer table from a plurality of question and answer tables according atleast to the classified text.
 3. The method of claim 1, wherein theidentified question and answer is output to a dialog management moduleand wherein the similarity computation further comprises:${{Sim}( {Q,{FAQ}_{j}} )} = \frac{\sum\limits_{i}^{\;}\; {q_{i} \cdot {faq}_{ji}}}{\sqrt{{\sum\limits_{i}^{\;}\; ( q_{i} )^{2}} + {c(0.5)}^{2}} \cdot \sqrt{\sum\limits_{i}\; ( {faq}_{ij} )^{2}}}$wherein the term faq_(ji) represents the questions in the question andanswer table and the term q_(i) represents the user utterance and c isthe set of words.
 4. The method of responding to utterances in a spokendialog service of claim 1, wherein the identified question and answeroutput is selected according to a most similar question to the receivedutterance.
 5. The method of responding to utterances of claim 1, whereinthe answer is selected from the selected question and answer table asbeing associated with a question that is the most similar to thereceived utterance.
 6. The method of responding to utterances of claim1, wherein each question and answer table includes questions andassociated answers.
 7. A computer-readable medium storing a computerprogram having instructions for controlling a computing device toperform the steps of: receiving a user utterance; identifying a questionand an answer selected from a question and answer table based on asimilarity computation that calculates a similarity between the userutterance and at least one question in the question and answer table anda set of words in the user utterance that are not found in a set ofunique words in the question and answer table; and presenting an answerto the user utterance from the identified question and answer.
 8. Thecomputer-readable medium of claim 7, wherein the instructions furthercomprise: classifying the recognized text associated with the userutterance; and selecting the question and answer table from a pluralityof question and answer tables according at least to the classified text.9. The computer-readable medium of claim 7, wherein the instructionsfurther comprise: outputting the identified question and answer to adialog management module and wherein the similarity computation furthercomprises:${{Sim}( {Q,{FAQ}_{j}} )} = \frac{\sum\limits_{i}^{\;}\; {q_{i} \cdot {faq}_{ji}}}{\sqrt{{\sum\limits_{i}^{\;}\; ( q_{i} )^{2}} + {c(0.5)}^{2}} \cdot \sqrt{\sum\limits_{i}\; ( {faq}_{ij} )^{2}}}$wherein the term faq_(ji) represents the questions in the question andanswer table and the term q_(i) represents the user utterance and c isthe set of words.
 10. The computer readable medium of claim 7, whereinthe identified question and answer output is selected according to amost similar question to the received utterance.
 11. Thecomputer-readable medium of claim 7, wherein the answer is selected fromthe selected question and answer table as being associated with thequestion that is most similar to the received utterance.
 12. Thecomputer-readable medium of claim 7, wherein the question and answertable includes questions and associated answers.
 13. A computing devicefor responding to utterances in a spoken dialog service, the computingdevice comprising: a module configured to receive a user utterance; amodule configured to identify a question and answer selected from aquestion and answer table based on a similarity computation thatcalculates a similarity between the user utterance and at least onequestion in the question and answer table, a set of words in the userutterance that are not found in a set of unique words in the questionand answer table; and a module configured to present an answer to theuser from the identified question and answer.
 14. The computing deviceof claim 13, further comprising: a module configured to classifyrecognized text associated with the user utterance; and a moduleconfigured to select the question and answer table from a plurality ofquestion and answer tables according at least to the classified text.15. The computing device of claim 13, wherein the identified questionand answer is output to a dialog management module, and whereinsimilarity computation further comprises:${{Sim}( {Q,{FAQ}_{j}} )} = \frac{\sum\limits_{i}^{\;}\; {q_{i} \cdot {faq}_{ji}}}{\sqrt{{\sum\limits_{i}^{\;}\; ( q_{i} )^{2}} + {c(0.5)}^{2}} \cdot \sqrt{\sum\limits_{i}\; ( {faq}_{ij} )^{2}}}$wherein the term faq_(ji) represents the questions in the question andanswer table and the term q_(i) represents the user utterance and c isthe set of words.
 16. The computing device of claim 13, wherein theidentified question and answer output is selected according to a mostsimilar question to the received utterance.
 17. The computing device ofclaim 13, wherein the answer is selected from the selected question andanswer table as being associated with a question that is the mostsimilar to the received utterance.
 18. The computing device of claim 13,wherein the question and answer table includes questions and associatedanswers.